Gong Eun Jeong, Bang Chang Seok, Lee Jae Jun
Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea.
Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea.
Biomimetics (Basel). 2024 Dec 22;9(12):783. doi: 10.3390/biomimetics9120783.
We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device.
A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to train, validation, and internal-test datasets in an 8:1:1 ratio. The major outcomes were as follows: 1. lesion classification accuracy in six categories: normal/atrophy/intestinal metaplasia/dysplasia/early/advanced gastric cancer; and 2. the prospective evaluation of classification accuracy in real-world procedures.
The internal-test lesion-classification accuracy was 93.8% (95% confidence interval: 93.4-94.2%); precision was 88.6%, recall was 88.3%, and F1 score was 88.4%. For the prospective performance test, the established model attained an accuracy of 93.3% (91.5-95.1%). The established model's lesion classification inference speed was 2-3 ms on GPU and 5-6 ms on CPU. The expert endoscopists reported no delays in lesion classification or any interference from the deep learning model throughout their exams.
We established a deep learning endoscopic image classification model to automatically classify all stages of gastric carcinogenesis using an edge computing device.
我们之前开发了用于预测六种胃部病变类型的人工智能（AI）诊断算法。然而，这需要大量的计算资源。将AI集成到医疗设备中已从集中式模型发展到去中心化的边缘计算设备。在本研究中，创建了一种深度学习内镜图像分类模型，以使用边缘计算设备对胃癌发生的所有阶段进行自动分类。
回顾性收集了总共15910张内镜图像，并以8:1:1的比例随机分配到训练、验证和内部测试数据集。主要结果如下：1. 六类病变的分类准确率：正常/萎缩/肠化生/发育异常/早期/进展期胃癌；2. 在实际操作中对分类准确率的前瞻性评估。
内部测试的病变分类准确率为93.8%（95%置信区间：93.4 - 94.2%）；精确率为88.6%，召回率为88.3%，F1分数为88.4%。对于前瞻性性能测试，所建立的模型准确率达到93.3%（91.5 - 95.1%）。所建立模型的病变分类推理速度在GPU上为2 - 3毫秒，在CPU上为5 - 6毫秒。专家内镜医师报告在整个检查过程中病变分类没有延迟，也没有受到深度学习模型的任何干扰。
我们建立了一种深度学习内镜图像分类模型，以使用边缘计算设备对胃癌发生的所有阶段进行自动分类。
